Overview

Dataset statistics

Number of variables18
Number of observations10495
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory136.0 B

Variable types

Numeric11
Boolean3
Categorical4

Alerts

df_index is highly correlated with UserID and 1 other fieldsHigh correlation
UserID is highly correlated with df_index and 1 other fieldsHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Daily_Avg_mins_spend_on_traveling_pageHigh correlation
montly_avg_comment_on_company_page is highly correlated with df_index and 1 other fieldsHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
df_index is highly correlated with UserID and 1 other fieldsHigh correlation
UserID is highly correlated with df_index and 1 other fieldsHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with total_likes_on_outofstation_checkin_received and 1 other fieldsHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
montly_avg_comment_on_company_page is highly correlated with df_index and 1 other fieldsHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
df_index is highly correlated with UserIDHigh correlation
UserID is highly correlated with df_indexHigh correlation
df_index is highly correlated with UserID and 2 other fieldsHigh correlation
UserID is highly correlated with df_index and 2 other fieldsHigh correlation
Yearly_avg_view_on_travel_page is highly correlated with total_likes_on_outofstation_checkin_received and 1 other fieldsHigh correlation
preferred_location_type is highly correlated with df_index and 1 other fieldsHigh correlation
total_likes_on_outofstation_checkin_received is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
montly_avg_comment_on_company_page is highly correlated with df_index and 2 other fieldsHigh correlation
working_flag is highly correlated with montly_avg_comment_on_company_pageHigh correlation
Daily_Avg_mins_spend_on_traveling_page is highly correlated with Yearly_avg_view_on_travel_page and 1 other fieldsHigh correlation
df_index has unique values Unique
UserID has unique values Unique
week_since_last_outstation_checkin has 916 (8.7%) zeros Zeros

Reproduction

Analysis started2022-05-15 07:54:57.228572
Analysis finished2022-05-15 07:55:39.961824
Duration42.73 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6266.086899
Minimum0
Maximum11759
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:40.096825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile708.4
Q13436.5
median6512
Q39135.5
95-th percentile11234.3
Maximum11759
Range11759
Interquartile range (IQR)5699

Descriptive statistics

Standard deviation3344.964079
Coefficient of variation (CV)0.5338202507
Kurtosis-1.138682017
Mean6266.086899
Median Absolute Deviation (MAD)2822
Skewness-0.1580657996
Sum65762582
Variance11188784.69
MonotonicityStrictly increasing
2022-05-15T13:25:40.294828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
48791
 
< 0.1%
48551
 
< 0.1%
110001
 
< 0.1%
89531
 
< 0.1%
28121
 
< 0.1%
7651
 
< 0.1%
69101
 
< 0.1%
48631
 
< 0.1%
110081
 
< 0.1%
Other values (10485)10485
99.9%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
81
< 0.1%
101
< 0.1%
121
< 0.1%
131
< 0.1%
ValueCountFrequency (%)
117591
< 0.1%
117581
< 0.1%
117571
< 0.1%
117561
< 0.1%
117551
< 0.1%
117541
< 0.1%
117531
< 0.1%
117521
< 0.1%
117511
< 0.1%
117501
< 0.1%

UserID
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct10495
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1006267.087
Minimum1000001
Maximum1011760
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:40.499789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1000709.4
Q11003437.5
median1006513
Q31009136.5
95-th percentile1011235.3
Maximum1011760
Range11759
Interquartile range (IQR)5699

Descriptive statistics

Standard deviation3344.964079
Coefficient of variation (CV)0.003324131458
Kurtosis-1.138682017
Mean1006267.087
Median Absolute Deviation (MAD)2822
Skewness-0.1580657996
Sum1.056077308 × 1010
Variance11188784.69
MonotonicityStrictly increasing
2022-05-15T13:25:40.719792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10014731
 
< 0.1%
10064861
 
< 0.1%
10085051
 
< 0.1%
10023641
 
< 0.1%
10003171
 
< 0.1%
10064621
 
< 0.1%
10105601
 
< 0.1%
10085131
 
< 0.1%
10023721
 
< 0.1%
10003251
 
< 0.1%
Other values (10485)10485
99.9%
ValueCountFrequency (%)
10000011
< 0.1%
10000021
< 0.1%
10000031
< 0.1%
10000041
< 0.1%
10000051
< 0.1%
10000061
< 0.1%
10000091
< 0.1%
10000111
< 0.1%
10000131
< 0.1%
10000141
< 0.1%
ValueCountFrequency (%)
10117601
< 0.1%
10117591
< 0.1%
10117581
< 0.1%
10117571
< 0.1%
10117561
< 0.1%
10117551
< 0.1%
10117541
< 0.1%
10117531
< 0.1%
10117521
< 0.1%
10117511
< 0.1%

Buy_ticket
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
False
8793 
True
1702 
ValueCountFrequency (%)
False8793
83.8%
True1702
 
16.2%
2022-05-15T13:25:40.876832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Yearly_avg_view_on_travel_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct330
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.2906146
Minimum92.5
Maximum464
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:41.012825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum92.5
5-th percentile182
Q1232
median271
Q3325
95-th percentile410
Maximum464
Range371.5
Interquartile range (IQR)93

Descriptive statistics

Standard deviation68.15361998
Coefficient of variation (CV)0.2422889938
Kurtosis-0.3658223759
Mean281.2906146
Median Absolute Deviation (MAD)45
Skewness0.426846911
Sum2952145
Variance4644.915916
MonotonicityNot monotonic
2022-05-15T13:25:41.216824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255169
 
1.6%
262163
 
1.6%
270160
 
1.5%
232137
 
1.3%
217136
 
1.3%
247123
 
1.2%
240120
 
1.1%
225118
 
1.1%
285117
 
1.1%
264116
 
1.1%
Other values (320)9136
87.1%
ValueCountFrequency (%)
92.58
0.1%
1353
 
< 0.1%
1368
0.1%
1376
 
0.1%
1383
 
< 0.1%
1402
 
< 0.1%
1413
 
< 0.1%
1424
 
< 0.1%
1437
 
0.1%
14419
0.2%
ValueCountFrequency (%)
4641
 
< 0.1%
4631
 
< 0.1%
4622
 
< 0.1%
4612
 
< 0.1%
4603
< 0.1%
4592
 
< 0.1%
4581
 
< 0.1%
4573
< 0.1%
4565
< 0.1%
4557
0.1%

preferred_device
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
Mobile
9387 
Laptop
1108 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMobile
2nd rowMobile
3rd rowMobile
4th rowMobile
5th rowMobile

Common Values

ValueCountFrequency (%)
Mobile9387
89.4%
Laptop1108
 
10.6%

Length

2022-05-15T13:25:41.424792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T13:25:41.533789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
mobile9387
89.4%
laptop1108
 
10.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct7565
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28157.21441
Minimum3570
Maximum76837.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:41.679826image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3570
5-th percentile5751.5
Q116323
median28178
Q340529.5
95-th percentile49881.8
Maximum76837.125
Range73267.125
Interquartile range (IQR)24206.5

Descriptive statistics

Standard deviation14118.77389
Coefficient of variation (CV)0.5014265148
Kurtosis-1.180304254
Mean28157.21441
Median Absolute Deviation (MAD)12007
Skewness-0.005765259566
Sum295509965.2
Variance199339776.2
MonotonicityNot monotonic
2022-05-15T13:25:41.893792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1151511
 
0.1%
378709
 
0.1%
241859
 
0.1%
185509
 
0.1%
401108
 
0.1%
449058
 
0.1%
290157
 
0.1%
313257
 
0.1%
228907
 
0.1%
51457
 
0.1%
Other values (7555)10413
99.2%
ValueCountFrequency (%)
35702
< 0.1%
35771
< 0.1%
35781
< 0.1%
36052
< 0.1%
36111
< 0.1%
36141
< 0.1%
36181
< 0.1%
36201
< 0.1%
36211
< 0.1%
36311
< 0.1%
ValueCountFrequency (%)
76837.1252
< 0.1%
525121
< 0.1%
525091
< 0.1%
524981
< 0.1%
524951
< 0.1%
524871
< 0.1%
524791
< 0.1%
524741
< 0.1%
524691
< 0.1%
524651
< 0.1%

yearly_avg_Outstation_checkins
Real number (ℝ≥0)

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.282229633
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2022-05-15T13:25:42.109792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q314
95-th percentile26
Maximum29
Range28
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.567415788
Coefficient of variation (CV)1.0344335
Kurtosis-0.3409570568
Mean8.282229633
Median Absolute Deviation (MAD)4
Skewness0.9701407273
Sum86922
Variance73.40061329
MonotonicityNot monotonic
2022-05-15T13:25:42.280829image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
13794
36.2%
2844
 
8.0%
10617
 
5.9%
9340
 
3.2%
7336
 
3.2%
3336
 
3.2%
8320
 
3.0%
5261
 
2.5%
4256
 
2.4%
6236
 
2.2%
Other values (19)3155
30.1%
ValueCountFrequency (%)
13794
36.2%
2844
 
8.0%
3336
 
3.2%
4256
 
2.4%
5261
 
2.5%
6236
 
2.2%
7336
 
3.2%
8320
 
3.0%
9340
 
3.2%
10617
 
5.9%
ValueCountFrequency (%)
29184
1.8%
28160
1.5%
2789
0.8%
26172
1.6%
25172
1.6%
24201
1.9%
23189
1.8%
22133
1.3%
21133
1.3%
20172
1.6%

member_in_family
Real number (ℝ≥0)

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.924916627
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size41.1 KiB
2022-05-15T13:25:42.452788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q34
95-th percentile4
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.042129254
Coefficient of variation (CV)0.3562936612
Kurtosis1.278599148
Mean2.924916627
Median Absolute Deviation (MAD)1
Skewness-0.003403818051
Sum30697
Variance1.086033381
MonotonicityNot monotonic
2022-05-15T13:25:42.593792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
34109
39.2%
42858
27.2%
21988
18.9%
11197
 
11.4%
5333
 
3.2%
1010
 
0.1%
ValueCountFrequency (%)
11197
 
11.4%
21988
18.9%
34109
39.2%
42858
27.2%
5333
 
3.2%
1010
 
0.1%
ValueCountFrequency (%)
1010
 
0.1%
5333
 
3.2%
42858
27.2%
34109
39.2%
21988
18.9%
11197
 
11.4%

preferred_location_type
Categorical

HIGH CORRELATION

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
Beach
2424 
Financial
1892 
Historical site
1856 
Medical
1463 
Other
1307 
Other values (2)
1553 

Length

Max length15
Median length8
Mean length8.64916627
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFinancial
2nd rowFinancial
3rd rowOther
4th rowFinancial
5th rowMedical

Common Values

ValueCountFrequency (%)
Beach2424
23.1%
Financial1892
18.0%
Historical site1856
17.7%
Medical1463
13.9%
Other1307
12.5%
Entertainment917
 
8.7%
Trekking636
 
6.1%

Length

2022-05-15T13:25:42.767792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T13:25:42.888792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
beach2424
19.6%
financial1892
15.3%
historical1856
15.0%
site1856
15.0%
medical1463
11.8%
other1307
10.6%
entertainment917
 
7.4%
trekking636
 
5.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Distinct97
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.02486899
Minimum3
Maximum147
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:43.119792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile41
Q157
median75
Q393
95-th percentile109
Maximum147
Range144
Interquartile range (IQR)36

Descriptive statistics

Standard deviation21.78220177
Coefficient of variation (CV)0.2903330864
Kurtosis-0.7372633742
Mean75.02486899
Median Absolute Deviation (MAD)18
Skewness-0.07430993798
Sum787386
Variance474.4643138
MonotonicityNot monotonic
2022-05-15T13:25:43.320800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96175
 
1.7%
90173
 
1.6%
66171
 
1.6%
56169
 
1.6%
72167
 
1.6%
80166
 
1.6%
60163
 
1.6%
91161
 
1.5%
87160
 
1.5%
61159
 
1.5%
Other values (87)8831
84.1%
ValueCountFrequency (%)
329
0.3%
3124
 
0.2%
3243
0.4%
3334
0.3%
3435
0.3%
3539
0.4%
3649
0.5%
3744
0.4%
3860
0.6%
3957
0.5%
ValueCountFrequency (%)
1473
 
< 0.1%
1257
 
0.1%
1243
 
< 0.1%
1238
 
0.1%
12210
 
0.1%
12111
0.1%
12010
 
0.1%
11924
0.2%
11824
0.2%
11725
0.2%

total_likes_on_outofstation_checkin_received
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct5323
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6393.32051
Minimum1009
Maximum16575.125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:43.540804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum1009
5-th percentile2136
Q12948
median4948
Q38398.5
95-th percentile16575.125
Maximum16575.125
Range15566.125
Interquartile range (IQR)5450.5

Descriptive statistics

Standard deviation4344.355261
Coefficient of variation (CV)0.6795146988
Kurtosis0.2918270917
Mean6393.32051
Median Absolute Deviation (MAD)2192
Skewness1.164557238
Sum67097898.75
Variance18873422.63
MonotonicityNot monotonic
2022-05-15T13:25:43.737804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16575.125822
 
7.8%
237711
 
0.1%
23429
 
0.1%
24049
 
0.1%
24379
 
0.1%
25708
 
0.1%
23878
 
0.1%
23808
 
0.1%
27938
 
0.1%
20968
 
0.1%
Other values (5313)9595
91.4%
ValueCountFrequency (%)
10092
< 0.1%
10141
< 0.1%
10171
< 0.1%
10501
< 0.1%
10511
< 0.1%
10522
< 0.1%
10551
< 0.1%
10581
< 0.1%
10601
< 0.1%
10612
< 0.1%
ValueCountFrequency (%)
16575.125822
7.8%
165671
 
< 0.1%
165611
 
< 0.1%
165051
 
< 0.1%
164951
 
< 0.1%
164911
 
< 0.1%
164871
 
< 0.1%
164811
 
< 0.1%
164801
 
< 0.1%
164771
 
< 0.1%

week_since_last_outstation_checkin
Real number (ℝ≥0)

ZEROS

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.208194378
Minimum0
Maximum11
Zeros916
Zeros (%)8.7%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:43.926802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile9
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.614157393
Coefficient of variation (CV)0.8148375955
Kurtosis-0.04194211834
Mean3.208194378
Median Absolute Deviation (MAD)2
Skewness0.9118349129
Sum33670
Variance6.833818876
MonotonicityNot monotonic
2022-05-15T13:25:44.298786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12733
26.0%
31605
15.3%
21502
14.3%
4993
 
9.5%
0916
 
8.7%
5643
 
6.1%
6585
 
5.6%
7545
 
5.2%
9410
 
3.9%
8384
 
3.7%
Other values (2)179
 
1.7%
ValueCountFrequency (%)
0916
 
8.7%
12733
26.0%
21502
14.3%
31605
15.3%
4993
 
9.5%
5643
 
6.1%
6585
 
5.6%
7545
 
5.2%
8384
 
3.7%
9410
 
3.9%
ValueCountFrequency (%)
1154
 
0.5%
10125
 
1.2%
9410
 
3.9%
8384
 
3.7%
7545
 
5.2%
6585
 
5.6%
5643
6.1%
4993
9.5%
31605
15.3%
21502
14.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
False
7548 
True
2947 
ValueCountFrequency (%)
False7548
71.9%
True2947
 
28.1%
2022-05-15T13:25:44.424785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

montly_avg_comment_on_company_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct33
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.35940924
Minimum11
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:44.567789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12
Q118
median23
Q328
95-th percentile37
Maximum43
Range32
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.394105407
Coefficient of variation (CV)0.3165364899
Kurtosis-0.142348812
Mean23.35940924
Median Absolute Deviation (MAD)5
Skewness0.4775622465
Sum245157
Variance54.67279477
MonotonicityNot monotonic
2022-05-15T13:25:44.785786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
23620
 
5.9%
22574
 
5.5%
24552
 
5.3%
25548
 
5.2%
20527
 
5.0%
21519
 
4.9%
19500
 
4.8%
18494
 
4.7%
26476
 
4.5%
17439
 
4.2%
Other values (23)5246
50.0%
ValueCountFrequency (%)
11321
3.1%
12324
3.1%
13312
3.0%
14379
3.6%
15301
2.9%
16346
3.3%
17439
4.2%
18494
4.7%
19500
4.8%
20527
5.0%
ValueCountFrequency (%)
43213
2.0%
4227
 
0.3%
4130
 
0.3%
4046
 
0.4%
3973
 
0.7%
3891
0.9%
3777
 
0.7%
36119
1.1%
35175
1.7%
34177
1.7%

working_flag
Boolean

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
False
8883 
True
1612 
ValueCountFrequency (%)
False8883
84.6%
True1612
 
15.4%
2022-05-15T13:25:44.951793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
3.0
3309 
4.0
3071 
2.0
2157 
1.0
1958 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row4.0
3rd row2.0
4th row3.0
5th row4.0

Common Values

ValueCountFrequency (%)
3.03309
31.5%
4.03071
29.3%
2.02157
20.6%
1.01958
18.7%

Length

2022-05-15T13:25:45.091787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T13:25:45.203787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
3.03309
31.5%
4.03071
29.3%
2.02157
20.6%
1.01958
18.7%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

number_of_adults
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size82.1 KiB
0.0
4496 
1.0
4265 
2.0
1115 
3.0
619 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.04496
42.8%
1.04265
40.6%
2.01115
 
10.6%
3.0619
 
5.9%

Length

2022-05-15T13:25:45.352825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2022-05-15T13:25:45.456786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
0.04496
42.8%
1.04265
40.6%
2.01115
 
10.6%
3.0619
 
5.9%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Daily_Avg_mins_spend_on_traveling_page
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct34
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.67393997
Minimum0
Maximum33
Zeros36
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size82.1 KiB
2022-05-15T13:25:45.614824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q18
median12
Q318
95-th percentile31
Maximum33
Range33
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.985312791
Coefficient of variation (CV)0.5839803895
Kurtosis-0.1291400463
Mean13.67393997
Median Absolute Deviation (MAD)5
Skewness0.7152073135
Sum143508
Variance63.76522037
MonotonicityNot monotonic
2022-05-15T13:25:45.783789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10994
 
9.5%
9599
 
5.7%
8585
 
5.6%
6558
 
5.3%
7487
 
4.6%
11475
 
4.5%
13474
 
4.5%
14448
 
4.3%
12441
 
4.2%
15427
 
4.1%
Other values (24)5007
47.7%
ValueCountFrequency (%)
036
 
0.3%
1296
2.8%
2130
 
1.2%
3195
 
1.9%
4304
2.9%
5390
3.7%
6558
5.3%
7487
4.6%
8585
5.6%
9599
5.7%
ValueCountFrequency (%)
33369
3.5%
3295
 
0.9%
3169
 
0.7%
3065
 
0.6%
29127
 
1.2%
28128
 
1.2%
27107
 
1.0%
26126
 
1.2%
25130
 
1.2%
24156
1.5%

Interactions

2022-05-15T13:25:36.541793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:02.571414image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:07.870980image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:13.586673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:17.139675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:20.337677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:23.203788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:26.109788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:28.882790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:31.437804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:33.896790image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:36.757786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:03.137705image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:08.330566image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:13.899673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:17.452676image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:20.607684image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:23.474788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:26.391784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:29.133787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:31.640834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:34.129788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:36.976804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:03.634886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:08.729922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:14.224675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:17.773671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:20.840673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:23.723786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:26.669788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:29.381786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:31.842832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:34.384791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:37.194786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:04.084777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:09.184677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:14.547675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:18.079674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:21.105675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:23.937788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:26.950789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:29.637787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:32.049824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:34.614791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:37.422825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:04.607364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:09.730681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:14.881674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:18.408674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:21.365675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:24.216793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:27.215789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:29.887825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:32.273792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:34.854822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:37.632820image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:05.089458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:10.247675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:15.137674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:18.694672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:21.585678image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:24.497786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:27.460827image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:30.105825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:32.482832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:35.077824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:37.818825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:05.540846image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:10.809675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:15.412673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:18.936674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:21.791700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:24.709785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:27.683788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:30.305825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:32.711800image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:35.270825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:38.033832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:06.058506image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:11.344673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:15.785677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:19.226695image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:22.031672image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:24.996785image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:27.948791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:30.539788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:32.988802image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:35.487788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:38.256824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:06.567312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:12.025677image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:16.229673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:19.506673image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:22.307441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:25.247788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:28.202786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:30.766786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:33.226804image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:35.739787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:38.456788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:07.015501image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:12.566674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:16.471675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:19.762680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:22.579447image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:25.461789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:28.417824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:30.982834image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:33.435788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:35.938825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:38.663828image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:07.414420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:13.141681image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:16.795680image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:20.043675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:22.871439image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:25.725789image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:28.647791image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:31.214786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:33.679787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2022-05-15T13:25:36.152824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2022-05-15T13:25:45.972825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-15T13:25:46.425784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-15T13:25:46.856824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-15T13:25:47.272824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-05-15T13:25:47.564792image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-15T13:25:39.053787image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-15T13:25:39.676825image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexUserIDBuy_ticketYearly_avg_view_on_travel_pagepreferred_devicetotal_likes_on_outstation_checkin_givenyearly_avg_Outstation_checkinsmember_in_familypreferred_location_typeYearly_avg_comment_on_travel_pagetotal_likes_on_outofstation_checkin_receivedweek_since_last_outstation_checkinfollowing_company_pagemontly_avg_comment_on_company_pageworking_flagtravelling_network_ratingnumber_of_adultsDaily_Avg_mins_spend_on_traveling_page
001000001Yes307.0Mobile38570.012Financial94.05993.08.0Yes11.0No1.00.08.0
111000002No367.0Mobile9765.011Financial61.05130.01.0No23.0Yes4.01.010.0
221000003Yes277.0Mobile48055.012Other92.02090.06.0Yes15.0No2.00.07.0
331000004No247.0Mobile48720.014Financial56.02909.01.0Yes11.0No3.00.08.0
441000005No202.0Mobile20685.011Medical40.03468.09.0No12.0No4.01.06.0
551000006No240.0Mobile35175.012Financial79.03068.00.0No13.0No3.00.08.0
681000009No285.0Mobile7560.0233Financial44.09526.00.0No21.0Yes2.00.010.0
7101000011No262.0Mobile28315.0163Medical84.02426.00.0No13.0No3.01.06.0
8121000013No232.0Mobile23450.0261Financial31.02911.01.0No17.0No4.01.05.0
9131000014No255.0Mobile47110.0192Medical93.02661.00.0No11.0No3.01.03.0

Last rows

df_indexUserIDBuy_ticketYearly_avg_view_on_travel_pagepreferred_devicetotal_likes_on_outstation_checkin_givenyearly_avg_Outstation_checkinsmember_in_familypreferred_location_typeYearly_avg_comment_on_travel_pagetotal_likes_on_outofstation_checkin_receivedweek_since_last_outstation_checkinfollowing_company_pagemontly_avg_comment_on_company_pageworking_flagtravelling_network_ratingnumber_of_adultsDaily_Avg_mins_spend_on_traveling_page
10485117501011751No231.0Mobile16423.0284Historical site96.03845.01.0No26.0No2.00.012.0
10486117511011752Yes383.0Mobile14399.0283Other58.010910.06.0Yes28.0No2.01.023.0
10487117521011753No302.0Mobile25317.0241Other79.012093.00.0No24.0No1.01.029.0
10488117531011754No247.0Mobile11418.053Historical site99.09983.01.0No28.0No2.00.016.0
10489117541011755No210.0Mobile40886.053Other53.03024.02.0No32.0No4.00.014.0
10490117551011756No279.0Laptop30987.0232Historical site58.02616.04.0No36.0No3.01.023.0
10491117561011757No305.0Mobile21510.061Historical site55.010041.04.0No30.0No1.01.011.0
10492117571011758No214.0Mobile5478.043Beach103.06203.03.0Yes40.0Yes2.01.012.0
10493117581011759No382.0Laptop35851.023Historical site83.05444.03.0No32.0No4.00.020.0
10494117591011760No270.0Mobile22025.083Historical site104.04470.02.0No29.0No1.00.014.0